Andrew is great at teaching. Quality of education is absolutely for 5 stars, but I am giving 4 because of technical difficulties with Jupiter notebook. Often happened that I wrote some code and it could not save, it just displayed error, so I had to copy code to my notepad and rerun the Jupiter notebook, and than copy the code back.

I really like the content but I believe that it is about time the final assignment of this course is updated to Tensorflow 2. There is no point in enforcing learning outdated software... For the massive revenue that this course is generating, the minimal effort to update one Jupyter Notebook should not be too much of a burden...

The course was a very focused approach towards introducing and familiarizing us with the importance of tuning hyperparameters and their impact on the performance. Although, I personally feel like the Tensorflow exercise could have been more detailed and could have used more explanation. I found that exercise somewhat confusing.

The explanation is just as good as the previous course. The reason I give 4 star is that the notebook use TF version 1 instead of 2. Given syntax of 1 and 2 shows great difference, at least I believe so, it would be better that the notebook can be updated. For the rest of the course, very good!!! Suitable for beginners in DL.

This course is well taught.

Andrew Ng takes you through the material without error and in a very acceptable pace.

The exercises are very do-able.

They do not challenge hard, but take you by the hand and show you how to implement and improve your neural networks.

The final assignment is a very good tutorial on TensorFlow actually :)

Compared to previous module, this one was more of a cookbook and I expected more mathematics in terms of why each optimization work.

Overall, it was still a very interesting hands on approach, finishing with TensorFlow is a bit more difficult to apprehend as all the previous exercices were done in a very different way (Numpy).

The course content is great as always. It introduces all the concepts in consize manner. The final assignment however fairly rudimentary. Would have been more beneficial to me and perhaps other students, to go through writing the training code rather than having to figure out what tf.transpose or tf.nn.softmax functions are.

First of all thankyou to Prof Andrew and team. This course is really good. I learned a lot of new things. Week 1 & 2 programming assinments are really good but I was not able to understand well the tensorflow introduction assignment in week 3. Rest the knowledge given through this course2 of the specialization is very good.

Most of the course is great, good overview of different methods and techniques with practical examples. However the TensorFlow programming part is rather confusing, lacking in sufficient explanation of the syntax and overlapping names of python and tensorflow variables which end up producing near impossible to debug errors.

This is an amazing course which dwells into the nuances of fine tuning your neural network model. The content of the course is too good. Programming assignments was a bit off. It was really straightforward. Programming assignments could have been more challenging. This will make sure that the concepts are learned properly.

The content is generally great and helpful, but the grader did not show me why the result is incorrect, and i constantly had to reload jupyter notebook. I think there should be less template for the exercise so that we have more thinking to do, but the expected result should be maintained so that we know what we did wrong.

The course explains the reasons and intuition behind tuning hyperparameters and why/how regularization techniques work well when training on large data sets. The only reason I am giving this a 4 star is because the tensorflow introduction seems a little too sparse and could be done better.

Thanks again, team deeplearning.ai

The contents are actually good, and it doesn't require a very extensive prior knowledge, so it's even suitable for people with little experience in programming or math. However, despite being a course that has been out for over 2 years, there are still some subtitle issues (in English), and typos on a clarification slide.

Well explained..sometimes jumps a bit. I felt lost a couple of times. But I got through it and I'd say this is deifnitely one of the top courses out there.

If they included some optional videos on how this could relate to having a career in this area that'd be very helpful (i.e. what level we need to be able to code at).

This course continues to build on foundations from course 1 of the specialization. Hyperparameter tuning and Regularization methods are quite imperative for optimizing ML models. This course covers these concepts in addition to providing a good foundation for Tensorflow library. Overall, a good course by Prof. Andrew!

The course was very helpful as now I understand optimization techniques and all the parameters of neural networks. Unfortunately, the course has not answered my question how to tune the whole bunch of hyperparameters from the scratch, what is the correct order and logic of the full ANN tuning, not just one parameter.

A very good follow on to the first course with continued excellent organization and hands on assignments that give you practical exposure to working on deep learning problems including a basic introduction to Tensorflow along with practical guidelines on Hyperparameter tuning among other deep learning related topics.

Hi,

This course does a really good job in introducing the optimization techniques. Prof. Andrew Ng has structured his lectures well.

Can I kindly suggest that this course can incorporate, for each optimization, 1 scenario that is applicable & 1 that isn't? That will emphasize the scope of the optimization.

Thanks,

Uday

Still some errors in the assignments, particularly in Week 3. Otherwise, a good course. A lot of good topics for practical use when building real-life neural networks. Some seem fairly cutting-edge. Good, brief, introduction to TensorFlow at the tail-end of Week 3 and in the programming assignment for that week.

The course content is really great and the theoretical concepts (or their intuition to a larger extent) have been explained pretty well. But there are some errors in the programming exercise on Tensor Flow which makes it confusing since the people who take this course are new to both deep learning and tensor flow.

This is a great course. The only reason for not giving it 5 stars is the notebook platform for grading coding assignments. It is flawed in that attempts to save often error out so a submission often doesn't contain the latest state of the code. This causes sections to be graded incorrectly - very frustrating.

Course material and Lectures is perfect!

May be would like to have less supervised programming assignments with less hints of how to implement code as it was too easy! May be it would be good to have pretty guided assignments during main part of the course, but something more individual and demanding at the end.

I think it is important to understand the basics and that is why it is really cool that they show you how to implement a neural net from sratch. Moreover, the last part on tensorflow shows you how to do it in real life. Nevetheless, a lot of work remains to be done to learn properly how to use tensorflow and NN

Andrew Ng is great at explaining the theory and practical aspects of DL concepts. I applaud him for making the lectures so accessible. I also really liked that he provided what is typically done by practitioners. My only feedback I have is that the quiz and programming assignment could have been more rigorous.

the programming assignments are too easy. Though I understand that they're supposed to guide someone who may be a total beginning in ML and DL, I feel there is still too much hand-holding by marking exactly the changes need to be done and the formula either embedded in the comments or in the description above